- People with chronic kidney disease (CKD) are more likely to get heart disease.
- Using proteomics, the analysis of proteins, researchers developed a model to predict cardiovascular disease in CKD patients.
- The model was found to be more accurate than current methods for establishing risk.
- Researchers also identified several proteins that could be developed into future therapies.
Cardiovascular disease (CVD) is the leading cause of death among people with chronic kidney disease (CKD).
CKD has five
About half of CKD patients in stage 4 and 5 have CVD according to a 2021
“Rates of cardiovascular disease are extraordinarily high in patients with chronic kidney disease,” Dr. Nisha Bansal, an associate professor in the Division of Nephrology at the University of Washington School of Medicine, explained to Medical News Today. “And this risk extends beyond sort of what we consider traditional risk factors for cardiovascular disease so there’s something we believe different about cardiovascular pathophysiology in this specific population.”
Health practitioners have limited tools for measuring cardiac risk for CKD patients. In 2013, the American College of Cardiology and the American Heart Association, developed the Pooled Cohort Equation (PCE) to assess cardiovascular risk.
However, the original
An effort led by researchers in the Perelman School of Medicine at the University of Pennsylvania has developed a new risk model for cardiovascular disease in CKD patients. The researchers say it is more accurate than current methods of measuring cardiac risk in these individuals.
A paper about the research was published in European Heart Journal.
The researchers developed a model to predict cardiovascular risk using
To develop their model, the researchers studied nearly 5,000 proteins from 2,667 participants with CKD from the Chronic Renal Insufficiency Cohort (CRIC), a prospective study of adults with CKD conducted at seven U.S. clinical centers, as well as a cohort from Atherosclerosis Risk in Communities (ARIC), a prospective epidemiologic study conducted in four U.S. communities.
One of the strengths of the model and the study was that researchers used a large number of participants from different sites across the county, said Bansal, who was not involved in the research.
Researchers used machine learning methods to choose 32 proteins to include in their proteomic risk model. Those proteins were determined to be the ones that best indicated a CKD patient’s risk level of cardiovascular disease.
“They really focused on biology and mechanisms of disease by using this sort of broad based approach to identifying proteins that may identify novel biological pathways that contribute to the risk of cardiovascular disease in patients with kidney disease,” Bansal said.
Participants selected for this study from the CRIC had cryopreserved plasma samples available for proteomic analysis. Selected participants were between the ages of 21 and 74 with CKD.
Participants in end stage renal disease and who were on dialysis were excluded. Individuals who at the beginning of the study self-reported a history of coronary heart disease, myocardial infarction, stroke or heart failure or had documented history of those events were excluded.
The final cohort had 2,182 participants.
Compared to ARIC participants, CRIC participants were somewhat younger, more likely to be male, and more likely to be Black. CRIC participants were also more likely to have a history of hypertension and diabetes and less likely to be active smokers than ARIC participants.
Total cholesterol levels were higher in ARIC participants than in CRIC participants.
Over a 10-year follow up period, there were 459 cardiovascular events among the CRIC cohort and 173 cardiovascular events in the ARIC cohort
After researchers developed a proteomic risk model for incident cardiovascular risk in the participants, they validated the model using 390 participants from the ARIC cohort who all had CKD.
Additionally, researchers figured the participants’ 2013 PCE. They also identified participants’ history of hypertension, diastolic blood pressure, proteinuria and estimated glomerular filtration rate (eFGR), a score that measures kidney function.
“They were trying to look at how these biological pathways compare with clinical prediction models, in terms of predicting cardiovascular events,” Bansal explained.
The researchers said the proteomic cardiovascular risk model was more accurate than both the PCE and a modified PCE that included eFGR scores at predicting a CKD patient’s risk for experiencing a cardiac event.
“I think the study does advance the field,” Bansal commented.
Participants in the highest measure of predicted risk had an observed incident cardiovascular event rate of 60% over a decade.
Nancy Mitchell, RN, a registered nurse and contributing writer for AssistedLivingCenter.com who has more than 37 years of experience treating people with chronic kidney disease and chronic cardiovascular conditions, is hopeful that the study might lead to “improving the treatment options for heart disease.”
“Scientists may consider conducting more research into how the proteins detected in the bloodwork connect to heart disease and how they can use these findings to create more targeted medications for heart disease,” she told Medical News Today.
In their paper, the researchers explain that knowing a CKD patient’s CVD risk would be useful to health practitioners because it could help them to determine which patients would benefit from expensive medications designed to reduce the risk of experiencing adverse cardiovascular events.
“If you’re able to predict who’s… at the highest risk of developing cardiovascular disease, there’s changes in clinical management you can make from the onset to help mitigate this risk of subsequent cardiovascular disease,” Bansai said. “You can start or change medications to focus more on cardiovascular risk reduction. You could also. . . change lifestyle, whether it’s diet or physical activity, change the type of follow up and the clinical care they’re getting. They may need more intense medical therapies or medical attention compared with those who have lower risk of cardiovascular disease.”
Through their work, the researchers identified several individual proteins associated with a cardiovascular outcome that may be used in future research to identify cardiac risk or to be used for therapeutic strategies. Half of the proteins selected for the model had not previously been associated with heart disease.
“They’ve identified a few biological pathways that may contribute [to] the risk of cardiovascular disease in this specific population,” Bansal said. “So I think more investigation of those pathways and potential therapies that target those pathways is what’s needed next.”
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